Binary hashing using siamese neural networks
Abin Jose, Shen Yan, Iris Heisterklaus · 2017
With the growth in multimedia data, it is the need of the hour to have methods for efficient storage and quick retrieval. In this work, we propose an approach for learning binary codes for fast image retrieval. We use a siamese architecture with two parallel feed forward branches but with a shared weight for the generation of binary codes. The training data is divided into similar and dissimilar pairs. The network tries to learn the weights such that it minimizes the distance between similar image pairs and maximizes the distance between dissimilar image pairs. The binary codes are formed by squashing the neural network output through a sigmoid activation function. The training with sigmoid hashing constrains the output of each node in the final fully connected layer to either 0 or 1. We have compared the retrieval performance of our approach with other state-of-the-art hashing methods and our method shows significant improvement.